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NASA NTRSDate not supplied

A Census of Severe Weather as Observed From Aqua: Visible/IR and Passive-Microwave Perspectives of Severe Convection

Severe weather phenomena represent the extreme upper end of the spectrum of convection and precipitation and tend to be highly localized and relatively rare compared to the rest of the distribution, but they can cause damage and loss disproportionate to their scale and frequency. Fortunately, severe convection exhibits distinct signatures in spaceborne remote-sensing datasets (e.g. overshooting cloud tops in visible/IR, or brightness temperature depressions in passive-microwave imagery). Leveraging these signatures individually has become a long-established practice to detect, analyze and establish climatologies of severe thunderstorms, especially in instances where traditional ground-based data may be unavailable. Spaceborne visible/IR and passive-microwave approaches are not without their pitfalls, however: passive-microwave channels have large footprints and exhibit non-uniform beam filling. Visible/IR instruments have fine horizontal resolution but are limited by their insensitivity to processes occurring below cloud top. To address this, we investigate the nearly simultaneous and colocated MODIS (visible/IR) and AMSR-E (passive-microwave) onboard the Aqua satellite to leverage both datasets together and assess the extent to which these datasets can be combined to improve severe thunderstorm detection. We pair AMSR-E and MODIS signatures of severe convection with ground-based weather radar, severe weather reports, and environmental parameters defined by the MERRA-2 reanalysis in six different geographical regimes throughout the Aqua domain. We present a census of potentially severe convective storms and their environments as seen by multiple instruments simultaneously, investigating how MODIS and AMSR-E signatures may be used together to diagnose storm properties and processes, and how the interrelationships between the signatures varies seasonally and geographically. Using statistical machine learning analysis, we aim to quantify the optimal MODIS and AMSR-E parameter sets for discriminating severe from non-severe storm cells and assess what improvement (if any) in detection results from combining the IR, visible, and microwave datasets.

Sarah Bang↗
NASA NTRS2003

Spectral behavior of hydrated sulfate salts: implications for Europa mission spectrometer design

Remote sensing of the surface of Europa with near-infrared instruments has suggested the presence of hydrated materials, including sulfate salts. Attention has been focused on these salts for the information they might yield regarding the evolution of a putative interior ocean, and the evaluation of its astrobiological potential. These materials exhibit distinct infrared absorption features due to bound water. The interactions of this water with the host molecules lead to fine structure that can be used to discriminate among these materials on the basis of their spectral behavior. This fine structure is even more pronounced at the low temperatures prevalent on icy satellites. Examination of hydrated sulfate salt spectra measured under cryogenic temperature conditions provides realistic constraints for future remote-sensing missions to Europa. In particular, it suggests that a spectrometer system capable of 2-5 nm spectral resolution or better, with a spatial resolution approaching 100 m, would be able to differentiate among proposed hydrated surface materials, if present, and constrain their distributions across the surface. Such information would provide valuable insights into the evolutionary history of Europa.

Astronomy↗
NASA NTRS1988

Real time thermal imaging of high temperature semiconductor melts

A real time thermal imaging system with temperature resolution better than + or - 1 C and spatial resolution of better than 0.5 mm was developed and applied to the analysis of melt surface thermal field distributions in both Czochralski and liquid encapsulated Czochralski (LEC) growth configurations. The melt is viewed in near normal incidence by a high resolution charge coupled device camera to which is attached a very narrow bandpass filter. The resulting image is digitized and processed using a pipelined pixel processor operating at an effective 40 million operations per second thus permitting real time high frequency spatial and temporal filtering of the high temperature scene. A multi-pixel averaging algorithm was developed which permits localized, low noise sensing of temperature variations at any location in the hot zone as a function of time. This signial is used to implement initial elements of a feedforward growth control scheme which is aimed at reducing disturbances to the melt caused by the batch nature of the growth process. The effect of magnetic melt stabilization on radial melt temperature distributions was measured using this technique. Problems associated with residual internal reflections and non-optimized path geometry are discussed.

Wargo, Michael J.↗
NASA NTRS2001

Polar Cirrus Cloud Properties Through Long-Term Lidar and Radiometer Observations

In comparison to mid latitude cloud cover, knowledge of polar cirrus and other cloud cover is limited. The interpretations of satellite-based cloud imaging and retrievals in polar regions have major problems due to factors such as darkness and extreme low temperatures. Beginning in 2002 a NASA orbiting lidar instrument, GLAS, (Geoscience Laser Altimeter System) will unambiguously define cloud type and fraction with good coverage of polar regions. Active laser sensing gives the spatial and temporal distribution of clouds and diamond dust. In preparation for, and supplementing the GLAS measurements are ground based MP (micro pulse) lidar experiments providing continuous profiling. MP lidar installations have been operating at the South Pole since December 1999 and at the Atmospheric Radiation Measurement (ARM) program arctic site since 1996. Both at the ARM Barrow, Alaska site and at the South Pole station, Fourier-transform interferometers also observe clouds in the wavelength intervals between approximately 5 and 18 microns. Spectral instruments can yield cloud microphysical properties with additional information from lidar about the vertical extent of clouds being modeled. We examine the simultaneous lidar and spectral data from both Barrow and South Pole, to obtain cloud properties (optical depth, particle size) by the use of both instruments. The results have applications to interpretation of current satellite data, and GLAS measurements when available.

Spinhirne, James↗
NASA NTRS2018

Intercomparison of Precipitation Estimates over the Arctic Ocean and Its Peripheral Seas from Reanalyses

Precipitation over the Arctic Ocean has a significant impact on the basin-scale freshwater and energy budgets but is one of the most poorly constrained variables in atmospheric reanalyses. Precipitation controls the snow cover on sea ice, which impedes the exchange of energy between the ocean and atmosphere, inhibiting sea ice growth. Thus, accurate precipitation amounts are needed to inform sea ice modeling, especially for the production of thickness estimates from satellite altimetry freeboard data. However, obtaining a quantitative estimate of the precipitation distribution in the Arctic is notoriously difficult because of a number of factors, including a lack of reliable, long-term in situ observations; difficulties in remote sensing over sea ice; and model biases in temperature and moisture fields and associated uncertainty of modeled cloud microphysical processes in the polar regions. Here, we compare precipitation estimates over the Arctic Ocean from eight widely used atmospheric reanalyses over the period 2000–16 (nominally the ‘‘new Arctic’’). We find that the magnitude, frequency, and phase of precipitation vary drastically, although interannual variability is similar. Reanalysis-derived precipitation does not increase with time as expected; however, an increasing trend of higher fractions of liquid precipitation (rainfall) is found. When compared with drifting ice mass balance buoys, three reanalyses (ERA-Interim, MERRA, and NCEP R2) produce realistic magnitudes and temporal agreement with observed precipitation events, while two products [MERRA, version 2 (MERRA-2), and CFSR] show large, implausible magnitudes in precipitation events. All the reanalyses tend to produce overly frequent Arctic precipitation. Future work needs to be undertaken to determine the specific factors in reanalyses that contribute to these discrepancies in the new Arctic.

Mcpartland, Linette N.↗
NASA NTRSDate not supplied

The Future of Oaks in the Santa Monica Mountains: A Case Study in Using Remote Sensing Data for Species Distributions Models

The Woolsey Fire began on November 8, 2018, and lasted for almost two weeks, during which it burned almost 100,000 acres of valuable landscape and habitat, including a vast area of woodland. The persistence of key woodland species provides aesthetic, monetary, and ecological value to the landscape through carbon sequestration, air temperature moderation, and erosion mitigation, among other ecosystem services. This study investigated the impact of the Woolsey Fire on native woodland species distributions and identified areas suitable for restoration within the Santa Monica Mountains National Recreation Area. The team partnered with the Resource Conservation District of the Santa Monica Mountains; National Park Service, Santa Monica Mountains National Recreation Area; California Department of Parks and Recreation, Los Angeles County Division; County of Los Angeles Fire Department, Prevention Services Bureau, Forestry Division; County of Los Angeles Department of Regional Planning; and the University of Montana. The Earth observations used include data from Landsat 8 Operational Land Imager, NASA ER-2 Jet Airborne Visible InfraRed Imaging Spectrometer, Shuttle Radar Topography Mission, and RapidEye. The team produced maps of burn severity from the Woolsey Fire, its impact on plant species distributions, and habitat suitability projections for 2050 and 2099 to assist partners in prioritizing areas for restoration. A plant community classification was successfully created using Multiple Endmember Spectral Mixture Analysis (MESMA). Overall accuracy was assessed at 90.54% by comparing the classification to validation pixels derived from ground truth information provided by our partners.

Roger Ly↗
NASA NTRSDate not supplied

Update on Earth Skin Temperature Monitoring From 16 Years of IASI Satellite Observations

Surface parameters such as emissivity and skin temperature have been retrieved from IASI measurements of last 16 years. Monthly and spatially gridded surface skin temperature is produced to show the phenomena of its natural variability, which is also reflected in the surface emissivity and/or soil moisture content derived from the same time series of measurements. The anomalies of surface skin temperature are used to estimate its trend. The IASI global surface skin temperature trend is comparable with the NASA GISTEMP5 global surface air temperature trend. The trend of IASI global surface skin temperature reports that an approximate 0.027 K/yr. increase has evolved during 2007–2023. Attention has been given to the arctic region where an average warming rate is near 3 times higher than the global average. In some arctic regions, the rapid warming rate has reached 0.2-0.3 K/yr. which is about 10 times higher than the global average. Geophysical distribution of the surface temperature trend in the arctic region is emphasized and reported.

Global warming↗
NASA NTRS1982

Interpretation of satellite-observed thunderstorm anvil structure

The radiative characteristics of an anvil plume sensed by satellite radiometers are studied to discern the reason for an observed 'V' pattern of colder temperatures and a corresponding internal warm region. The measured black body temperature is affected by the ice water content and particle distributions in the cloud layer. Attention is given to 'V' anvil structure examined by Haynesfield et al. (1979). The GOES satellite IR data are treated with a plane parallel geometric radiative transfer model and a particle trajectory model is used to simulate ice particle concentrations downwind from a strongly divergent cloud top. Output from the two models indicate the 'V' pattern arises from divergence in the storm anvil. The particle concentration was elevated within the V-shaped region, causing lower temperatures in the V arms. The V is produced by strong upper level flow as well as strong mass and moisture divergence in the storm outflow.

Heymsfield, G. M.↗
NASA NTRS1988

A methodology for mapping forest latent heat flux densities using remote sensing

Surface temperatures and reflectances of an upper elevation Sierran mixed conifer forest were monitored using the Thematic Mapper Simulator sensor during the summer of 1985 in order to explore the possibility of using remote sensing to determine the distribution of solar energy on forested watersheds. The results show that the method is capable of quantifying the relative energy allocation relationships between the two cover types defined in the study. It is noted that the method also has the potential to map forest latent heat flux densities.

Pierce, Lars L.↗
NASA NTRS2003

Accessing and Understanding MODIS Data

The National Aeronautics and Space Administration (NASA) launched the Terra satellite in December 1999, as part of the Earth Science Enterprise promotion of interdisciplinary studies of the integrated Earth system. Aqua, the second satellite from the series of EOS constellation, was launched in May 2002. Both satellites carry the MODerate resolution Imaging Spectroradiometer (MODIS) instrument. MODIS data are processed at the Goddard Space Flight Center, Greenbelt, MD, and then archived and distributed by the Distributed Active Archive Centers (DAACs). Data products from the MODIS sensors present new challenges to remote sensing scientists due to specialized production level, data format, and map projection. MODIS data are distributed as calibrated radiances and as higher level products such as: surface reflectance, water-leaving radiances, ocean color and sea surface temperature, land surface kinetic temperature, vegetation indices, leaf area index, land cover, snow cover, sea ice extent, cloud mask, atmospheric profiles, aerosol properties, and many other geophysical parameters. MODIS data are stored in HDF- EOS format in both swath format and in several different map projections. This tutorial guides users through data set characteristics as well as search and order interfaces, data unpacking, data subsetting, and potential applications of the data. A CD-ROM with sample data sets, and software tools for working with the data will be provided to the course participants.

Leptoukh, Gregory↗
NASA NTRS2009

Lunar Neutral Exposphere Properties from Pickup Ion Analysis

Composition and structure of neutral constituents in the lunar exosphere can be determined through measurements of phase space distributions of pickup ions borne from the exosphere [1]. An essential point made in an early study [ 1 ] and inferred by recent pickup ion measurements [2, 3] is that much lower neutral exosphere densities can be derived from ion mass spectrometer measurements of pickup ions than can be determined by conventional neutral mass spectrometers or remote sensing instruments. One approach for deriving properties of neutral exospheric source gasses is to first compare observed ion spectra with pickup ion model phase space distributions. Neutral exosphere properties are then inferred by adjusting exosphere model parameters to obtain the best fit between the resulting model pickup ion distributions and the observed ion spectra. Adopting this path, we obtain ion distributions from a new general pickup ion model, an extension of a simpler analytic description obtained from the Vlasov equation with an ion source [4]. In turn, the ion source is formed from a three-dimensional exospheric density distribution, which can range from the classical Chamberlain type distribution to one with variable exobase temperatures and nonthermal constituents as well as those empirically derived. The initial stage of this approach uses the Moon's known neutral He and Na exospheres to deriv e He+ and Na+ pickup ion exospheres, including their phase space distributions, densities and fluxes. The neutral exospheres used are those based on existing models and remote sensing studies. As mentioned, future ion measurements can be used to constrain the pickup ion model and subsequently improve the neutral exosphere descriptions. The pickup ion model is also used to estimate the exosphere sources of recently observed pickup ions on KAGUYA [3]. Future missions carrying ion spectrometers (e.g., ARTEMIS) will be able to study the lunar neutral exosphere with great sensitivity, yielding the necessary ion velocity spectra needed to further analysis of parent neutral exosphere properties.

Hartle, R. E.↗
NASA NTRS1987

Fiber optic and laser sensors IV; Proceedings of the Meeting, Cambridge, MA, Sept. 22-24, 1986

The conference presents papers on industrial uses of fiber optic sensors, point and distributed polarimetric optical fiber sensors, fiber optic electric field sensor technology, micromachined resonant structures, single-mode fibers for sensing applications, and measurement techniques for magnetic field gradient detection. Consideration is also given to electric field meter and temperature measurement techniques for the power industry, the calibration of high-temperature fiber-optic microbend pressure transducers, and interferometric sensors for dc measurands. Other topics include the recognition of colors and collision avoidance in robotics using optical fiber sensors, the loss compensation of intensity-modulating fiber-optic sensors, and an embedded optical fiber strain tensor for composite structure applications.

De Paula, Ramon P.↗
NASA NTRS1985

Characterizing terrestrial ecosystems and productivity from remote sensing data

Predictive relationships were studied between the leaf area index (LAI) of temperate coniferous forests and the canopy of reflective properties as sensed by satellites. Also, the relationship was examined between this sensible variable, LAI, and functional properties such as net primary productivity (NPP) and nitrogen mineralization. Leaf surface area is a locus of many important material and energy exchanges. If LAI can be reasonably estimated from remote sensing measurements, then it could be used with models to predict evapotranspiration, radiation interception, precipitation interception, and other ecosystem processes over large areas. Nineteen mature closed canopy forest stands were measured for leaf area index distributed along a temperature moisture gradient across Oregon. The LAI varies from 15.4 to 0.6. Infrared radiation is strongly scattered by leaves so that it penetrates deeply and its reflectance is proportional to LAI. Red radiation is strongly absorbed by chlorophyll and its reflectance is inversely related to LAI, becoming asymptotic at LAI values of about 3. The ratio of infrared to red radiation compensates for irradiance variations across this transect.

Peterson, David L.↗
NASA NTRS2010

Combining Hydrological Modeling and Remote Sensing Observations to Enable Data-Driven Decision Making for Devils Lake Flood Mitigation in a Changing Climate

This slide presentation reviews work to combine the hydrological models and remote sensing observations to monitor Devils Lake in North Dakota, to assist in flood damage mitigation. This reports on the use of a distributed rainfall-runoff model, HEC-HMS, to simulate the hydro-dynamics of the lake watershed, and used NASA's remote sensing data, including the TRMM Multi-Satellite Precipitation Analysis (TMPA) and AIRS surface air temperature, to drive the model.

Zhang, Xiaodong↗
NASA NTRS2016

Controlled Environments Enable Adaptive Management in Aquatic Ecosystems Under Altered Environments

Ecosystems worldwide are impacted by altered environment conditions resulting from climate, drought, and land use changes. Gaps in the science knowledge base regarding plant community response to these novel and rapid changes limit both science understanding and management of ecosystems. We describe how CE Technologies have enabled the rapid supply of gap-filling science, development of ecosystem simulation models, and remote sensing assessment tools to provide science-informed, adaptive management methods in the impacted aquatic ecosystem of the California Sacramento-San Joaquin River Delta. The Delta is the hub for California's water, supplying Southern California agriculture and urban communities as well as the San Francisco Bay area. The changes in environmental conditions including temperature, light, and water quality and associated expansion of invasive aquatic plants negatively impact water distribution and ecology of the San Francisco Bay/Delta complex. CE technologies define changes in resource use efficiencies, photosynthetic productivity, evapotranspiration, phenology, reproductive strategies, and spectral reflectance modifications in native and invasive species in response to altered conditions. We will discuss how the CE technologies play an enabling role in filling knowledge gaps regarding plant response to altered environments, parameterization and validation of ecosystem models, development of satellite-based, remote sensing tools, and operational management strategies.

Remote Sensing↗
NASA NTRS1987

Multiplatform sampling (ship, aircraft, and satellite) of a Gulf Stream warm core ring

The purpose of this paper is to demonstrate the ability to meet the need to measure distributions of physical and biological properties of the ocean over large areas synoptically and over long time periods by means of remote sensing utilizing contemporaneous buoy, ship, aircraft, and satellite (i.e., multiplatform) sampling strategies. A mapping of sea surface temperature and chlorophyll fields in a Gulf Stream warm core ring using the multiplatform approach is described. Sampling capabilities of each sensing system are discussed as background for the data collected by means of these three dissimilar methods. Commensurate space/time sample sets from each sensing system are compared, and their relative accuracies in space and time are determined. The three-dimensional composite maps derived from the data set provide a synoptic perspective unobtainable from single platforms alone.

Smith, Raymond C.↗
NASA NTRS2014

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

Modeling↗
NASA NTRS2024

Applications of Remote Sensing for Land Use Planning Scenarios with Suitability Analysis

In regions undergoing rapid urbanization, such as West Africa, land use planning (LUP) is vital to accommodate growing population and manage natural resources. Suitability analysis modeling is a widely used tool in LUP to determine the extent to which a land area is suitable for a designated purpose, but there is a gap in the integration of remote sensing time series data into land use decisions. The goal of this study was to incorporate remote sensing time series information with suitability analyses to inform LUP decisions in urban areas. In the study area of Kumasi, Ghana, land cover trends and land surface temperature (LST) from 2000 to 2019 were used to understand climate change trends. Suitability analyses determined the fitness of land areas for predetermined uses. These background processes informed a genetic algorithm to project plausible futures for three land use scenarios. One scenario represented current land use planning practices for addressing population growth, another scenario prioritized minimizing climate change impacts while also accommodating population growth, and the final scenario focused on both of these climate and population goals in addition to high density urban development. Each of these scenarios was successful in achieving population accommodation and respective climate change mitigation goals. The results for these scenarios provide insight into plausible land use distributions in 2050 based on different planning approaches. The genetic algorithm was able to effectively develop results for each scenario through the integration of remotely sensed trends and suitability models, providing a novel approach to land use decision-making.

remote sensing time series↗